Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
Download as PPTX, PDF2 likes647 views
Freed from the constraints of storage, network and memory, many big data analytics systems now are routinely revealing themselves to be compute bound. To compensate, big data analytic systems often result in wide horizontal sprawl (300-node Spark or NoSQL clusters are not unusual!)— to bring in enough compute for the task at hand. High system complexity and crushing operational costs often result. As the world shifts from physical to virtual assets and methods of engagement, there is an increasing need for systems of intelligence to live alongside the more traditional systems of record and systems of analysis. New approaches to data processing are required to support the real-time processing of data required to drive these systems of intelligence. Join 451 Research and Kinetica to learn: •An overview of the business and technical trends driving widespread interest in real-time analytics •Why systems of analysis need to be transformed and augmented with systems of intelligence bringing new approaches to data processing •How a new class of solution—a GPU-accelerated, scale out, in-memory database–can bring you orders of magnitude more compute power, significantly smaller hardware footprint, and unrivaled analytic capabilities. •Hear how other companies in a variety of industries, such as financial services, entertainment, pharmaceutical, and oil and gas, benefit from augmenting their legacy systems with a modern analytics database.
![One of the things I like about
Kinetica is it gives us more of a
general-purpose use of the
technology. There has been a lot
of software created to answer
certain questions [but] highly
specialized tools have limited
functionality and are tuned to do
a certain workload.
"
Mark Ramsey, Chief Data Officer at GSK
BUSINESS OBJECTIVE
• Faster processing of transcriptomics to run simulations of
chemical reactions for drug discovery, research, and
development
NEW CAPABILITIES DELIVERED
• In-database processing to develop models, leveraging GPU
acceleration for performance, and direct access to CUDA APIs
via UDFs deployed within Kinetica
• Seek out signals from massive collection of drug targets
combined from external data, historical data from
experiments, ad clinical trials
SOLUTION OVERVIEW
• Kinetica running on-premises on a cluster of 7 HPE DL 380
servers
• Familiar relational database with GPU acceleration
LIFE SCIENCES : GENOMICS RESEARCH
CASE STUDY : ADVANCED IN-DATABASE ANALYTICS
43](https://image.slidesharecdn.com/201711kineticawebinaraslettt-171101234749/85/Powering-Real-Time-Big-Data-Analytics-with-a-Next-Gen-GPU-Database-43-320.jpg)
Ad
More Related Content
What's hot (20)
Similar to Powering Real-Time Big Data Analytics with a Next-Gen GPU Database (20)
Ad
Recently uploaded (20)
Ad
Powering Real-Time Big Data Analytics with a Next-Gen GPU Database
- 1. Powering real-time big data analytics with a next-gen GPU database November 1, 2017 Matt Aslett Research Director, Data Platforms and Analytics Channel 451 Research Dipti Borkar Vice President, Product Marketing Kinetica
- 2. Housekeeping Items 2 Questions? A copy of the presentation will be provided to all attendeesPresentation Slides Feedback To ask a question, click on the question button Don’t forget to leave feedback at the end of the webinar
- 3. Today’s speakers 3 Matt Aslett Research Director, Data Platforms and Analytics Channel, 451 Research Matt has overall responsibility for the data platforms and analytics research coverage, which includes operational and analytic databases, Hadoop, grid/cache, stream processing, search-based data platforms, data integration, data quality, data management, analytics, machine learning and advanced analytics. Matt's own primary area of focus includes data management, reporting and analytics, and exploring how the various data platforms and analytics technology sectors are converging in the form of next-generation data platforms. Dipti Borkar Vice President, Product Marketing, Kinetica Dipti has over 15 years experience in database technology across relational and non-relational databases. Prior to Kinetica, Dipti was Vice President of Product Marketing at Couchbase and held several leadership positions there including Head of Global Technical Sales and Head of Product Management. Earlier in her career Dipti was a part of the product team at MarkLogic and managed development teams at IBM DB2 where she started her career as a database software engineer. Dipti holds a Masters degree in Computer Science from the University of California, San Diego with a specialization in databases, and an MBA from the Haas School of Business at University of California, Berkeley.
- 4. Powering real-time big data analytics with a next-gen GPU database Matt Aslett Research Director, Data Platforms & Analytics
- 5. 451 Research is a leading IT research & advisory company 5 Founded in 2000 300+ employees, including over 120 analysts 2,000+ clients: Technology & Service providers, corporate advisory, finance, professional services, and IT decision makers 70,000+ IT professionals, business users and consumers in our research community Over 52 million data points published each quarter and 4,500+ reports published each year 3,000+ technology & service providers under coverage 451 Research and its sister company, Uptime Institute, are the two divisions of The 451 Group Headquartered in New York City, with offices in London, Boston, San Francisco, Washington DC, Mexico, Costa Rica, Brazil, Spain, UAE, Russia, Taiwan, Singapore and Malaysia Research & Data Advisory Events Go 2 Market
- 6. 6
- 7. Big data and beyond 7 • V is for various things… but does not define big data • To understand the trends driving ‘big data 451 Research focused beyond the nature of the data on what enterprises wanted to do with it
- 8. Big data and beyond 8 • V is for various things… but does not define big data • To understand the trends driving ‘big data 451 Research focused beyond the nature of the data on what enterprises wanted to do with it • Totality – storing and processing all data (or as much as is economically viable • Exploration – schema-free approaches to analyzing data to identify new patterns • Frequency – more frequent analysis of data to enable real-time decision making
- 9. Traditional systems of engagement and analysis 9
- 10. New systems of analysis 10
- 11. New systems of engagement 11
- 12. New systems of intelligence 12
- 13. New systems of intelligence 13
- 14. Emergence of GPU databases ▪ Potential customers that are doing deep learning and more advanced analytics on HPC systems that leverage GPU processors ▪ Data scientists or other specialists need to pull data from a system of record and load it into an HPC system to perform the analytics leveraging certain algorithms. 14
- 15. 15 Emergence of GPU databases • While HPC systems are well equipped to handle advanced analytics because they leverage GPUs, there is also a price to be paid as it requires moving the data from one system to the other. • GPU databases open up the door for machine learning, deep learning and other advanced analytical workloads to be run alongside BI workloads, within the same environment.
- 16. CPUs and GPUs • A CPU is a very good general processor, handling a variety of complex tasks well. • A GPU, is more specialized and can do certain tasks extremely well. • CPUs consist of multiple cores • GPUs consist of thousands of cores • CPUs geared for serial operations • GPUs geared for parallel operations ▪ Can be paired together for the greatest overall optimization 16
- 17. What’s required for analytics? 17 Methods Data Processing
- 18. CPUs for standard SQL-based BI 18 Methods Data Processing SQL CPU
- 19. GPUs extend analytical benefits 19 Methods Data Processing SQL CPU ML/DL GPU Some benefits of GPUs ▪ Performance, acceleration ▪ Data sets, large/scale ▪ Analytics, machine learning, deep learning ▪ Querying, real-time dashboards, reports ▪ Visualization, interactive, drill down
- 20. Key takeaways 20
- 22. Powering real-time big data analytics with a next-gen GPU database Dipti Borkar| VP, product Marketing| [email protected]
- 23. Company 80+, enterprise and startup expertise Awards Customers and Partners Investors $50m Series A June 2017 Ray Lane Company| Summary 2014 2016 23
- 24. Advances in Big Data Processing DATA WAREHOUSE RDBMS & Data Warehouse technologies enable organizations to store and analyze growing volumes of data on high performance machines, but at high cost. DISTRIBUTED STORAGE Hadoop and MapReduce enables distributed storage and processing across multiple machines. Storing massive volumes of data becomes more affordable, but performance is slow AFFORDABLE MEMORY Affordable memory allows for faster data read and write. HANA, MemSQL, & Exadata provide faster analytics. 1990 - 2000’s 2005… 2010… 2017… AT SCALE PROCESSING BECOMES THE BOTTLENECK GPU ACCELERATED COMPUTE GPU cores bulk process tasks in parallel - far more efficient for many data-intensive tasks than CPUs which process those tasks linearly. 24
- 25. GPU | Tale of Numbers 100x 75% Performance >100x gains over traditional RDBMS / NoSQL / In-Mem Databases Cores Modern GPUs can consist of up to 3000+ cores compared to 32 in a CPU Costs 75% reduction in infrastructure costs, licensing, staff, etc. More with Less Increase performance, throughput, capability while minimizing the costs to support the business GPUs are designed around thousands of small, efficient cores that are well suited to performing repeated similar instructions in parallel – making them ideal for the compute-intensive workloads required of large data sets. Performance Increase Infrastructure Cost Savings 4000vs. 32 25
- 26. Kinetica: Core 26 ANALYTICS DATABASE ACCELERATED BY GPUs KINETICA Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 GPU Accelerated Columnar In-memory Database HTTP Head Node Columnar in-memory database Data available much like a traditional RDBMS… rows, columns Data held in-memory; persisted to disk Interact with Kinetica through its native REST API, Java, Python, JavaScript, NodeJS, C++, SQL, etc… as well as with various connectors Native GIS & IP address object support VERY FAST: Ideal for OLAP workloads Typical hardware setup: 256GB - 1TB memory with 2-4 GPUs per node.
- 27. Kinetica Architecture 27 ETL / STREAM PROCESSING ON DEMAND SCALE OUT + 1TB MEM / 2 GPU CARDS SQL Native APIs PARALLELINGEST Geospatial WMS Custom Connectors In-Database Processing CUSTOM LOGIC BIDMach ML Libs BI DASHBOARDS BI / GIS / APPS CUSTOM APPS & GEOSPATIAL KINETICA ‘REVEAL’ STREAMINGDATAERP/CRM/ TRANSACTIONALDATA UDFs
- 28. The Kinetica cluster architecture VISUALIZATION via ODBC/JDBCAPIs Java API JavaScript API REST API C++ API Node.js API Python API OPEN SOURCE INTEGRATION Apache NiFi Apache Kafka Apache Spark Apache Storm GEOSPATIAL CAPABILITIES Geometric Objects Tracks Geospatial Endpoints WMS WKT KINETICA CLUSTER On-Demand Scale Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Server Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Server Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Server Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Server OTHER INTEGRATION Message Queues ETL Tools Streaming Tools 28
- 29. Parallel Ingest Provides High Performance Streaming 29 1 NODE (1TB/2GPU) PARALLEL INGEST 1 NODE (1TB/2GPU) 1 NODE (1TB/2GPU) Each node of the system can share the task of data ingest, provides more and faster throughput. It can always be made faster simply by adding more nodes.
- 30. 50-100x Faster on Queries with Large Datasets • Large retailer tested complex SQL queries on 3 years of retail data (150bn rows) • 10 node Kinetica cluster against 30TB+ cluster from next best alternative • GPU is able to perform many instructions in parallel. Huge performance gains on aggregations, group bys, joins, etc. • Kinetica sustained ingest of 1.3bn objects/minute with 70 attributes per row 30 WHEN COMPARED TO LEADING IN-MEMORY ALTERNATIVES
- 31. Combined Strengths and Capabilities
- 32. Kinetica | Combined Strengths and Capabilities Supercharge BI Taking advantage of the parallel nature of the GPU, Kinetica delivers low- latency, high-performance analytics on large and steaming data sets. Simultaneously ingest, explore, analyze, and visualize data within milliseconds to make critical decisions. User-defined functions (UDFs) allow for distributed custom compute directly from within the database. Easier to work with large geospatial data sets. Fast, Distributed Database Engine In-Database Analytics Native Geospatial & Visualization Pipeline 32
- 33. Copyright (C) 2017 451 Research LLC New systems of intelligence 33
- 34. Use Cases
- 35. FASTER BI WITH A GPU DATABASE 35 Tableau + Kinetica Kinetica combines GPU’s brute-force compute with the simplicity of a relational database for millisecond query response on massive data sets without extensive tuning. • Incredibly fast query performance. • Distributed design - ideal for large and streaming datasets. • SQL-92 compliant relational database – without limits. • More power means less need for tuning, indexing, and administration of the database. • No need to do pre-aggregation or build out cubes. • Reduce reliance on specialized skills to prep and set-up data.
- 36. 36 Rethink interaction between business analyst & data scientist SPECIALIZED AI/ DATA SCIENCE TOOLS SUBSET DATA SCIENTISTS BUSINESS USERS EXTRACT EXTRACTING DATA FOR AI IS EXPENSIVE AND SLOW ENTERPRISES STRUGGLE TO MAKE AI MODELS AVAILABLE TO BUSINESS ??? • MapReduce • Spark • NoSQL DBs • SQL Databases • DFS • CPU Compute Nodes • GPU Compute Nodes Proliferation of Hardware & Software Components
- 37. Kinetica | The Ideal Process – Consolidate the BI / AI stack 37 Monte Carlo Risk Custom Function 2 Custom Function 3 API EXPOSES CUSTOM FUNCTIONS WHICH CAN BE MADE AVAILABLE TO BUSINESS USERS BUSINESS USERS DATA SCIENTISTS UDFs • Analytics • AI/ML/Deep Learning • Power of in-memory SQL • Integrated CPU/GPU • Bomb with Streams Single Database Platform for AI + BI
- 38. AI & BI on One GPU-Accelerated Database HIGH PERFORMANCE ANALYTICS DATABASE UDF UDF UDF ODBC / JDBC Native REST API WMS BUSINESS INTELLIGENCE CUSTOM APPLICATIONS HIGH FIDELITY GEOSPATIAL PIPELINE MACHINE LEARNING & DEEP LEARNING GPU-ACCELERATED DATA SCIENCE PREDICTIVE MODELS e.g. Risk Management, Sales Volume, Fraud. BIDMach SQL DATA SCIENTISTS / DEVELOPERS BUSINESS USERS 38
- 39. Distributed Geospatial Pipeline 39 NATIVE VISUALIZATION IS DESIGNED FOR FAST MOVING, LOCATION-BASED DATA Native Geospatial Object Types • Points, Shapes, Tracks, Labels Native Geospatial Functions • Filters (by area, by series, by geometry, etc.) • Aggregation (histograms) • Geofencing - triggers • Video generation (based on dates/times) Generate Map Overlay Imagery (via WMS) • Rasterize points • Style based on attributes (class-break) • Heat maps
- 41. ENTERTAINMENT | Customer 360 41 CASE STUDY : BI ACCELERATION BUSINESS OBJECTIVE • Accelerate Tableau dashboards for faster customer 360 analytics NEW CAPABILITIES DELIVERED • 24X faster dashboard loads • 3.5X faster slice and dice, drilldowns, filters SOLUTION OVERVIEW • Tableau Server and Kinetica running on Google Cloud Platform • Kinetica accelerates EDW workload • Simply point to Kinetica using Tableau’s replace data source feature
- 42. 42 AD TECH | Real-time reporting & ad delivery CASE STUDY : REAL-TIME DATA AND ANALYTICS BUSINESS OBJECTIVE • Be first to market with game changing technologies that put publishers’ needs first • Support PubMatic’s real-time campaign reporting NEW CAPABILITIES DELIVERED • High-speed ingest, store, and persist data processing capabilities • Ad-hoc analytics on ad impression and bid data SOLUTION OVERVIEW • Kinetica considered as a functional replacement for a 40-node Apache Apex cluster -> smaller HW footprint • Hi-speed data ingestion via native Kafka integration • Python access to Kinetica data store for simplified data science discovery • Contributed fast data capabilities to long term retention and archive Hadoop Data Lake “At PubMatic, we are consistently focused on being early to market with leading technologies that put publishers’ needs first. Processing over one trillion ad impressions monthly, PubMatic provides omni-channel revenue automation technology for publishers and programmatic tools for media buyers. Leveraging leading edge data and technology innovation, Kinetica contributes high-speed ingest, store, and persist data processing capabilities in support of PubMatic’s real-time reporting and ad pacing engine.” - Vasu Cherlopalle, Vice President of Big Data and Analytics
- 43. One of the things I like about Kinetica is it gives us more of a general-purpose use of the technology. There has been a lot of software created to answer certain questions [but] highly specialized tools have limited functionality and are tuned to do a certain workload. " Mark Ramsey, Chief Data Officer at GSK BUSINESS OBJECTIVE • Faster processing of transcriptomics to run simulations of chemical reactions for drug discovery, research, and development NEW CAPABILITIES DELIVERED • In-database processing to develop models, leveraging GPU acceleration for performance, and direct access to CUDA APIs via UDFs deployed within Kinetica • Seek out signals from massive collection of drug targets combined from external data, historical data from experiments, ad clinical trials SOLUTION OVERVIEW • Kinetica running on-premises on a cluster of 7 HPE DL 380 servers • Familiar relational database with GPU acceleration LIFE SCIENCES : GENOMICS RESEARCH CASE STUDY : ADVANCED IN-DATABASE ANALYTICS 43
- 44. PIPELINE & WELL ANALYTICS 44 CASE STUDY : LOCATION BASED ANALYTICS BUSINESS OBJECTIVE • Augment SaaS offering to provide research data and analytics on oil and gas to energy investors and operators with geospatial query, visualization, and analytics NEW CAPABILITIES DELIVERED • Geospatial visualization and analytics of massive number of wells, pipelines by land ownership, region etc. • Custom visualizations and charts for data-driven insights • Embedded solution with seamless Node.js integration, GPU acceleration SOLUTION OVERVIEW • Kinetica running in RSEG’s Amazon Web Services VPC deployment
- 45. LOGISTICS | Workforce optimization BUSINESS OBJECTIVE • Deliver better business services, optimize operations, and save costs across 600,000 employees, 215,000 delivery vehicles, and deliver 500 million pieces of mail daily NEW CAPABILITIES DELIVERED • Real-time delivery and pickup notifications, shipment routing, just-in-time supplies • Real-time route optimization - route planning, rerouting • Geospatial analytics to uncover overlapping coverage areas, uncovered areas, and distribution bottlenecks SOLUTION OVERVIEW • USPS runs Kinetica as a 70 TB in-memory database on a HPE DL 380 200 node system. Each node consists of a single X86 blade server with 1TB RAM, 2 NVIDIA K80 GPUs • Kinetica collects, processes, and analyzes 200,000 messages per minute for real-time streaming analytics. 15,000 daily sessions with 5 9’s uptime 45
- 46. PERFORMANCE SCALABLE CONVERGED AI AND BI INDUSTRY-STANDARD CONNECTIVITY Distributed Columnar In-Memory Relational GPU Accelerated Ingest, Query, Compute Commodity Hardware On-premises or Cloud Scales to 100’s of TB Less Infrastructure More Compute Predictable, Linear Machine Learning Artificial Intelligence In-Database Self-Service Open Source Kafka, Storm, NiFi, Spark ODBC, JDBC ANSI SQL/92 API’s for Java, JS, C++, Python, Node.js, REST Summary | Kinetica GPU Accelerated Analytics 46
- 47. Thank you! Dipti Borkar | VP Product Marketing| [email protected]